## [1] "total tweets (English-only)"
## [1] 69197
## [1] "original non-retweets (raw count):"
## [1] 25031
## [1] "retweet counts (raw count):"
## [1] 44166
## [1] "tweets that are replies:"
## [1] 1328
## [1] "tweets that quote (retweet + added own text on top) tweets:"
## [1] 4079
## # A tibble: 6 x 3
## year tweets accounts
## <dbl> <int> <int>
## 1 2014 2251 1097
## 2 2015 3209 1215
## 3 2016 28254 13109
## 4 2017 10811 5188
## 5 2018 11701 4552
## 6 2019 12971 5592
## topic subtotal percent_total num_retweets percent_retweets
## 1 Abstinence 268 0.39 140 52.24
## 2 Condom 3024 4.37 1849 61.14
## 3 PrEP 13865 20.04 6682 48.19
## 4 Circumcision 969 1.40 450 46.44
## 5 PEP 782 1.13 297 37.98
## 6 Harm Reduction 2701 3.90 1568 58.05
## 7 Viral Load 2008 2.90 1274 63.45
## 8 Violence Against Women 4527 6.54 2620 57.87
## 9 EMTCT 271 0.39 152 56.09
## 10 Serosorting 5 0.01 1 20.00
## topic subtotal percent_total num_retweets percent_retweets
## 11 Microbicides 341 0.49 239 70.09
## 12 Sex Work 612 0.88 286 46.73
## 13 Testing 6856 9.91 3960 57.76
Note: 40066 tweets (including retweets) do not have our themes.
Not sure if this help us but see if there are terms we can use to refine our word lists.
## Joining, by = "word"
## # A tibble: 10 x 2
## word n
## <chr> <int>
## 1 hiv 8280
## 2 world 4764
## 3 people 4541
## 4 access 3673
## 5 hands 3622
## 6 million 3606
## 7 aids 3502
## 8 close 3405
## 9 gap 3380
## 10 action 3377
## # A tibble: 10 x 2
## word n
## <chr> <int>
## 1 women 3122
## 2 prevention 3074
## 3 girls 3054
## 4 infected 2869
## 5 infections 2846
## 6 services 2799
## 7 treatment 2666
## 8 urgent 2359
## 9 campaign 2143
## 10 health 1945
## # A tibble: 10 x 2
## word n
## <chr> <int>
## 1 efforts 1825
## 2 day 1749
## 3 join 1537
## 4 adults 1513
## 5 lives 1495
## 6 ensure 1273
## 7 support 1228
## 8 sexual 1178
## 9 leading 1156
## 10 worldwide 1152
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 6.26 Condom
## 2 2015 4.99 Condom
## 3 2016 4.03 Condom
## 4 2017 4.62 Condom
## 5 2018 5.43 Condom
## 6 2019 3.47 Condom
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 14.4 PrEP
## 2 2015 21.0 PrEP
## 3 2016 5.78 PrEP
## 4 2017 18.4 PrEP
## 5 2018 35.8 PrEP
## 6 2019 39 PrEP
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 3.47 Circumcision
## 2 2015 0.69 Circumcision
## 3 2016 1.08 Circumcision
## 4 2017 0.84 Circumcision
## 5 2018 1.51 Circumcision
## 6 2019 2.27 Circumcision
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 0.49 Abstinence
## 2 2015 0.81 Abstinence
## 3 2016 0.44 Abstinence
## 4 2017 0.42 Abstinence
## 5 2018 0.24 Abstinence
## 6 2019 0.25 Abstinence
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 0.4 EMTCT
## 2 2015 0.9 EMTCT
## 3 2016 0.47 EMTCT
## 4 2017 0.35 EMTCT
## 5 2018 0.09 EMTCT
## 6 2019 0.39 EMTCT
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 1.11 PEP
## 2 2015 0.65 PEP
## 3 2016 0.21 PEP
## 4 2017 0.47 PEP
## 5 2018 2.52 PEP
## 6 2019 2.54 PEP
## # A tibble: 6 x 3
## # Groups: year [6]
## year percent Topic
## <dbl> <dbl> <chr>
## 1 2014 8.8 Testing
## 2 2015 11.2 Testing
## 3 2016 6.95 Testing
## 4 2017 10.2 Testing
## 5 2018 10.8 Testing
## 6 2019 15.2 Testing
**Based on number of original tweet authorship.
## .
## HIV_Insight Sex_Worker_Hlth DrMbere Hlth_Literacy HIVIreland
## 3144 484 465 444 396
## UNAIDS EPICBrowardOrg Health_HIV2030 HopeAndHelpInc himmoderator
## 269 262 240 221 183
## itech_network one2oneKE GMIPartnership HIVpxresearch Prison_Health
## 170 169 162 152 139
## ProceedNCTSTA UNAIDS_AP AIDSFreeGen AniShakari RectalMicro
## 135 111 110 110 108
## # A tibble: 20 x 4
## retweet_count screen_name date text
## <int> <chr> <date> <chr>
## 1 907 UNAIDS 2016-12-01 "1.9 million adults became infected w…
## 2 493 accphivprn 2019-12-09 "Prevent HIV, you should #BabyYoda #H…
## 3 468 UN 2016-12-01 "The world must take urgent & imm…
## 4 329 UNAIDS 2016-11-30 "Join the #worldaidsday campaign and …
## 5 320 tMSMWellness 2019-03-12 "Trans guy/trans masc and into cis gu…
## 6 274 MissUniverse 2016-12-01 "Know your status. Get Tested. \n\nPo…
## 7 239 ourladyj 2019-06-19 "When folks don’t know their worth, t…
## 8 229 UN 2016-11-30 "#HIVprevention efforts must increase…
## 9 226 ChildtoChild 2016-12-02 "#childparticipation is crucial for e…
## 10 186 UNAIDS 2016-11-22 "More than 2 million people are infec…
## 11 184 AdoreDelano 2016-10-16 "I heart my baes ❤️ #RedDress #LGBTQC…
## 12 173 philredcross 2016-12-01 "#HandsUp for #HIVPrevention. Let's s…
## 13 161 UN_Women 2017-12-01 "There are 870,000 new HIV infections…
## 14 159 UNAIDS 2017-03-08 "New UNAIDS report shows urgent need …
## 15 152 UNAIDS 2016-10-03 "UNAIDS launches #WorldAIDSDay campai…
## 16 149 UNAIDS 2019-03-11 "#HIV was the leading cause of death …
## 17 145 GHDatState 2016-12-01 "Today is #WorldAIDSDay! Together we …
## 18 145 FirstLadyRwa… 2016-12-01 "Together, striving for zero new #HIV…
## 19 141 UNAIDS 2019-10-01 "#HIV was the leading cause of death …
## 20 126 UNFPA 2018-12-01 "Ensuring young people can access com…
NOTE: Follower count is based on their follower count at the time I collected the data (whenever that is)
## `summarise()` ungrouping output (override with `.groups` argument)
## Selecting by retweet_count
## # A tibble: 10 x 6
## screen_name retweet_count follower_count ratio tweet_authorship like_count
## <chr> <int> <int> <dbl> <int> <int>
## 1 UNAIDS 11239 258322 0.0435 269 9617
## 2 HIV_Insight 1880 17816 0.106 3144 2459
## 3 MichelSidibe 1551 39187 0.0396 47 1983
## 4 UN 908 12047848 0.0001 5 804
## 5 MissUniverse 705 1022563 0.0007 13 2661
## 6 UNAIDS_AP 687 11923 0.0576 111 652
## 7 HIVpxresearch 499 8778 0.0568 152 750
## 8 accphivprn 493 243 2.03 1 1390
## 9 AniShakari 470 5188 0.0906 110 757
## 10 HIVIreland 468 3564 0.131 396 447
## # A tibble: 10 x 6
## screen_name retweet_count follower_count ratio tweet_authorship like_count
## <chr> <int> <int> <dbl> <int> <int>
## 1 GMIPartners… 447 2705 1.65e-1 162 437
## 2 tMSMWellness 443 893 4.96e-1 4 642
## 3 UNFPA 437 214571 2.00e-3 9 552
## 4 kentbuse 408 10892 3.75e-2 69 435
## 5 DrMbere 346 589 5.87e-1 465 71
## 6 UN_Women 323 1792006 2.00e-4 3 281
## 7 frontlineai… 318 23541 1.35e-2 58 344
## 8 philredcross 308 552795 6.00e-4 6 255
## 9 GlobalFund 305 215735 1.40e-3 6 278
## 10 UNESCO 304 3197214 1.00e-4 5 265
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## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'
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## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'